Title: Data mining technology for machinery equipment and information networks

Authors: Li Yang; Yongqiang Wang; Jielong Geng

Addresses: School of Artificial Intelligence, Tangshan University, Tangshan, Hebei, China ' Department of Internet of Things Engineering, Tangshan University, Tangshan, Hebei, China ' Tangshan University, Tangshan, Hebei, China

Abstract: In order to predict the Rate of Penetration (ROP) during drilling, a new data mining technology for drilling ROP prediction is proposed. The weight of the ROP influencing factors determined by the Analytic Hierarchy Process (AHP) and the original data are used as the input information, after neural network training and iteration, the drilled ROP model is determined, finally, the model is used to simulate and predict the ROP to be drilled in the same area. Simulation results show that the Analytic Hierarchy Process-Back Propagation (AHP-BP) combined model established by the feedforward neural network principle can improve the iterative convergence speed and the reliability of the training results to a certain extent. Experimental results show that the predicted value of drilling speed is basically close to the actual value, the relative error is controlled within 10%, and the prediction effect is good with an accuracy of 98%.

Keywords: rate of penetration prediction; data mining; analytic hierarchy process; neural network; AHP-BP; analytic hierarchy process-back propagation; neural network.

DOI: 10.1504/IJIPT.2025.147110

International Journal of Internet Protocol Technology, 2025 Vol.18 No.1, pp.41 - 47

Received: 11 Oct 2024
Accepted: 23 Nov 2024
Published online: 10 Jul 2025 *